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Fulltext The diagnostic value of MRI segmentation technique for shoulder joint injuries based on deep learning

Dai L, Md Johar MG, Alkawaz MH

Sci Rep, 2024 Nov 21;14(1):28885.
PMID: 39572780 DOI: 10.1038/s41598-024-80441-y

This work is to investigate the diagnostic value of a deep learning-based magnetic resonance imaging (MRI) image segmentation (IS) technique for shoulder joint injuries (SJIs) in swimmers. A novel multi-scale feature fusion network (MSFFN) is developed by optimizing and integrating the AlexNet and U-Net algorithms for the segmentation of MRI images of the shoulder joint. The model is evaluated using metrics such as the Dice similarity coefficient (DSC), positive predictive value (PPV), and sensitivity (SE). A cohort of 52 swimmers with SJIs from Guangzhou Hospital serve as the subjects for this study, wherein the accuracy of the developed shoulder joint MRI IS model in diagnosing swimmers' SJIs is analyzed. The results reveal that the DSC for segmenting joint bones in MRI images based on the MSFFN algorithm is 92.65%, with PPV of 95.83% and SE of 96.30%. Similarly, the DSC for segmenting humerus bones in MRI images is 92.93%, with PPV of 95.56% and SE of 92.78%. The MRI IS algorithm exhibits an accuracy of 86.54% in diagnosing types of SJIs in swimmers, surpassing the conventional diagnostic accuracy of 71.15%. The consistency between the diagnostic results of complete tear, superior surface tear, inferior surface tear, and intratendinous tear of SJIs in swimmers and arthroscopic diagnostic results yield a Kappa value of 0.785 and an accuracy of 87.89%. These findings underscore the significant diagnostic value and potential of the MRI IS technique based on the MSFFN algorithm in diagnosing SJIs in swimmers.
Fulltext The role of ubiquitous metal ions in degradation of microplastics in hot-compressed water

Vo TP, Rintala J, Dai L, Oh WD, He C

Water Res, 2023 Oct 15;245:120672.
PMID: 37783176 DOI: 10.1016/j.watres.2023.120672

Hydrothermal processing (HTP) is an efficient thermochemical technology to achieve sound treatment and resource recovery of sewage sludge (SS) in hot-compressed subcritical water. However, microplastics (MPs) and heavy metals can be problematic impurities for high-quality nutrients recovery from SS. This study initiated hydrothermal degradation of representative MPs (i.e., polyethylene (PE), polyamide (PA), polypropylene (PP)) under varied temperatures (180-300 °C) to understand the effect of four ubiquitous metal ions (i.e., Fe3+, Al3+, Cu2+, Zn2+) on MPs degradation. It was found that weight loss of all MPs in metallic reaction media was almost four times of that in water media, indicating the catalytic role of metal ions in HTP. Especially, PA degradation at 300 °C was promoted by Fe3+ and Al3+ with remarkable weight loss higher than 95% and 92%, respectively, which was ca. 160 °C lower than that in pyrolysis. Nevertheless, PE and PP were more recalcitrant polymers to be degraded under identical condition. Although higher temperature thermal hydrolysis reaction induced severe chain scission of polymers to reinforce degradation of MPs, Fe3+ and Al3+ ions demonstrated the most remarkable catalytic depolymerization of MPs via enhanced free radical dissociation rather than hydrolysis. Pyrolysis gas chromatography-mass spectrometry (Py GC-MS) was further complementarily applied with GC-MS to reveal HTP of MPs to secondary MPs and nanoplastics. This fundamental study highlights the crucial role of ubiquitous metal ions in MPs degradation in hot-compressed water. HTP could be an energy-efficient technology for effective treatment of MPs in SS with abundant Fe3+ and Al3+, which will benefit sustainable recovery of cleaner nutrients in hydrochar and value-added chemicals or monomers from MPs.
Fulltext The effects of musical feedback training on metacognition and self-directed learning

Li W, Manoharan P, Cui X, Liu F, Liu K, Dai L

Front Hum Neurosci, 2023;17:1304929.
PMID: 38173798 DOI: 10.3389/fnhum.2023.1304929

INTRODUCTION: Metacognition and self-directed learning are key components in educational research, recognized for their potential to enhance learning efficiency and problem-solving skills. This study explores the effects of musical feedback training on these competencies.
METHODS: The study involved 84 preservice teachers aged 18 to 21. Participants were randomly assigned to either an experimental group, which received musical feedback training, or a control group.
RESULTS: The findings indicate that musical feedback training effectively improved metacognitive abilities. However, its impact on the readiness for self-directed learning was inconclusive. A notable difference in metacognition and self-directed learning readiness was observed between the experimental and control groups during the session, indicating a significant interaction effect. Furthermore, a positive correlation was identified between metacognition and self-directed learning.
DISCUSSION: These results contribute to educational discourse by providing empirical evidence on the utility of musical feedback training in fostering metacognition. They also highlight the importance of consistent and long-term engagement in self-directed learning practices. The significance of these findings advocates for incorporating music feedback training into music education curricula to enhance metacognition and improve overall learning efficiency.
Kinetic investigation on the catalytic pyrolysis of plastic fractions of waste electrical and electronic equipment (WEEE): A mathematical deconvolution approach

Gulshan S, Shafaghat H, Wang S, Dai L, Tang C, Fu W, et al.

Waste Manag, 2024 Jul 22;187:156-166.
PMID: 39043078 DOI: 10.1016/j.wasman.2024.07.015

Waste electrical and electronic equipment (WEEE) has become a critical environmental problem. Catalytic pyrolysis is an ideal technique to treat and convert the plastic fraction of WEEE into chemicals and fuels. Unfortunately, research using real WEEE remains relatively limited. Furthermore, the complexity of WEEE complicates the analysis of its pyrolytic kinetics. This study applied the Fraser-Suzuki mathematical deconvolution method to obtain the pseudo reactions of the thermal degradation of two types of WEEE, using four different catalysts (Al2O3, HBeta, HZSM-5, and TiO2) or without a catalyst. The main contributor(s) to each pseudo reaction were identified by comparing them with the pyrolysis results of the pure plastics in WEEE. The nth order model was then applied to estimate the kinetic parameters of the obtained pseudo reactions. In the low-grade electronics pyrolysis, the pseudo-1 reaction using TiO2 as a catalyst achieved the lowest activation energy of 92.10 kJ/mol, while the pseudo-2 reaction using HZSM-5 resulted in the lowest activation energy of 101.35 kJ/mol among the four catalytic cases. For medium-grade electronics, pseudo-3 and pseudo-4 were the main reactions for thermal degradation, with HZSM-5 and TiO2 yielding the lowest pyrolytic activation energies of 75.24 and 226.39 kJ/mol, respectively. This effort will play a crucial role in comprehending the pyrolysis kinetic mechanism of WEEE and propelling this technology toward a brighter future.
Fulltext Potentially toxic elements in human scalp hair around China's largest polymetallic rare earth ore mining and smelting area

Dai L, Deng L, Wang W, Li Y, Wang L, Liang T, et al.

Environ Int, 2023 Feb;172:107775.
PMID: 36739854 DOI: 10.1016/j.envint.2023.107775

There is a growing concern about human health of residents living in areas where mining and smelting occur. In order to understand the exposure to the potentially toxic elements (PTEs), we here identify and examine the cadmium (Cd), chromium (Cr), copper (Cu), manganese (Mn), nickel (Ni), lead (Pb) and zinc (Zn) in scalp hair of residents living in the mining area (Bayan Obo, n = 76), smelting area (Baotou, n = 57) and a reference area (Hohhot, n = 61). In total, 194 hair samples were collected from the volunteers (men = 87, women = 107) aged 5-77 years old in the three areas. Comparing median PTEs levels between the young and adults, Ni levels were significantly higher in adults living in the smelting area while Cr was highest in adults from the mining area, no significant difference was found for any of the elements in the reference area. From the linear regression model, no significant relationship between PTEs concentration, log10(PTEs), and age was found. The concentrations of Ni, Cd, and Pb in hair were significantly lower in the reference area when compared to both mining and smelting areas. In addition, Cu was significantly higher in the mining area when compared to the smelting area. Factor analysis (FA) indicated that men and women from the smelting area (Baotou) and mining area (Bayan Obo), respectively, had different underlying communality of log10(PTEs), suggesting different sources of these PTEs. Multiple factor analysis quantilized the importance of gender and location when combined with PTEs levels in human hair. The results of this study indicate that people living in mining and/or smelting areas have significantly higher PTEs (Cu, Ni, Cd, and Pb) hair levels compared to reference areas, which may cause adverse health effects. Remediation should therefore be implemented to improve the health of local residents in the mining and smelting areas.
Corrigendum to "Potentially toxic elements in human scalp hair around China's largest polymetallic rare earth ore mining and smelting area" [Environ. Int. 172 (2023) 107775]

Dai L, Deng L, Wang W, Li Y, Wang L, Liang T, et al.

Environ Int, 2024 Nov 22.
PMID: 39580246 DOI: 10.1016/j.envint.2024.109160
Fulltext Probiotic Bifidobacterium lactis Probio-M8 treated and prevented acute RTI, reduced antibiotic use and hospital stay in hospitalized young children: a randomized, double-blind, placebo-controlled study

Mageswary MU, Ang XY, Lee BK, Chung YF, Azhar SNA, Hamid IJA, et al.

Eur J Nutr, 2021 Nov 26.
PMID: 34825264 DOI: 10.1007/s00394-021-02689-8

PURPOSE: The development of probiotics has seen tremendous growth over the years, with health benefits ranging from gut health to respiratory. We thus aimed to investigate the effects of probiotic Bifidobacterium lactis Probio-M8 (2 × 1010 log CFU/day) against acute respiratory tract infections (RTI), use of antibiotics, hospitalization period and elucidate the possible mechanisms of action in hospitalized young children.
METHOD: A prospective, randomized, double-blind and placebo-controlled study was performed in RTI-hospitalized children. Patients were randomized to either the probiotic (n = 60, mean age 13.81 ± 0.90 months) or placebo (n = 60, mean age 12.11 ± 0.73 months) which were administered upon admission, continued during hospitalization and 4-week post-discharged. RTI and gut health parameters were assessed at these time points using validated questionnaires while concentrations of inflammatory cytokines were assessed via oral swabs.
RESULTS: Probio-M8 reduced the duration of nasal, pharyngeal and general flu-like symptoms compared to the placebo during the hospitalization period and 4-week post-discharged (P
Correction to: Probiotic Bifidobacterium lactis Probio-M8 treated and prevented acute RTI, reduced antibiotic use and hospital stay in hospitalized young children: a randomized, double-blind, placebo-controlled study

Mageswary MU, Ang XY, Lee BK, Chung YF, Azhar SNA, Hamid IJA, et al.

Eur J Nutr, 2022 Feb 05.
PMID: 35122153 DOI: 10.1007/s00394-022-02818-x
IDRiD: Diabetic Retinopathy - Segmentation and Grading Challenge

Porwal P, Pachade S, Kokare M, Deshmukh G, Son J, Bae W, et al.

Med Image Anal, 2020 01;59:101561.
PMID: 31671320 DOI: 10.1016/j.media.2019.101561

Diabetic Retinopathy (DR) is the most common cause of avoidable vision loss, predominantly affecting the working-age population across the globe. Screening for DR, coupled with timely consultation and treatment, is a globally trusted policy to avoid vision loss. However, implementation of DR screening programs is challenging due to the scarcity of medical professionals able to screen a growing global diabetic population at risk for DR. Computer-aided disease diagnosis in retinal image analysis could provide a sustainable approach for such large-scale screening effort. The recent scientific advances in computing capacity and machine learning approaches provide an avenue for biomedical scientists to reach this goal. Aiming to advance the state-of-the-art in automatic DR diagnosis, a grand challenge on "Diabetic Retinopathy - Segmentation and Grading" was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI - 2018). In this paper, we report the set-up and results of this challenge that is primarily based on Indian Diabetic Retinopathy Image Dataset (IDRiD). There were three principal sub-challenges: lesion segmentation, disease severity grading, and localization of retinal landmarks and segmentation. These multiple tasks in this challenge allow to test the generalizability of algorithms, and this is what makes it different from existing ones. It received a positive response from the scientific community with 148 submissions from 495 registrations effectively entered in this challenge. This paper outlines the challenge, its organization, the dataset used, evaluation methods and results of top-performing participating solutions. The top-performing approaches utilized a blend of clinical information, data augmentation, and an ensemble of models. These findings have the potential to enable new developments in retinal image analysis and image-based DR screening in particular.
Integrated image-based deep learning and language models for primary diabetes care

Li J, Guan Z, Wang J, Cheung CY, Zheng Y, Lim LL, et al.

Nat Med, 2024 Jul 19.
PMID: 39030266 DOI: 10.1038/s41591-024-03139-8

Primary diabetes care and diabetic retinopathy (DR) screening persist as major public health challenges due to a shortage of trained primary care physicians (PCPs), particularly in low-resource settings. Here, to bridge the gaps, we developed an integrated image-language system (DeepDR-LLM), combining a large language model (LLM module) and image-based deep learning (DeepDR-Transformer), to provide individualized diabetes management recommendations to PCPs. In a retrospective evaluation, the LLM module demonstrated comparable performance to PCPs and endocrinology residents when tested in English and outperformed PCPs and had comparable performance to endocrinology residents in Chinese. For identifying referable DR, the average PCP's accuracy was 81.0% unassisted and 92.3% assisted by DeepDR-Transformer. Furthermore, we performed a single-center real-world prospective study, deploying DeepDR-LLM. We compared diabetes management adherence of patients under the unassisted PCP arm (n = 397) with those under the PCP+DeepDR-LLM arm (n = 372). Patients with newly diagnosed diabetes in the PCP+DeepDR-LLM arm showed better self-management behaviors throughout follow-up (P